Geoffrey Ginsburg

Overview:

Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.

Positions:

Professor of Medicine

Medicine, Cardiology
School of Medicine

Director of Duke Center for Applied Genomics and Precision Medicine

Duke Center for Applied Genomics and Precision Medicine
School of Medicine

Professor in Pathology

Pathology
School of Medicine

Professor of Biostatistics and Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Professor in the School of Nursing

School of Nursing
School of Nursing

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 1984

Boston University

Ph.D. 1984

Boston University

Medical Resident, Medicine

Beth Israel Deaconess Medical Center

Fellow in Cardiology, Medicine

Beth Israel Deaconess Medical Center

Research Fellow in Cardiology, Medicine

Children's Hospital Boston

Grants:

Building and Deploying a Genomic-Medicine Risk Assessment Model for Diverse Primary Care Populations.

Administered By
Duke Center for Applied Genomics and Precision Medicine
Awarded By
National Institutes of Health
Role
Investigator
Start Date
End Date

The IGNITE II CC: Engagement, Coordination, Demonstration, and Dissemination

Administered By
Duke Center for Applied Genomics and Precision Medicine
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

The IGNITE II CC: Engagement, Coordination, Demonstration, and Dissemination

Administered By
Duke Center for Applied Genomics and Precision Medicine
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

The Role of Junctophilin Type 2 in Cardiac Node Automaticity

Administered By
Pediatrics, Cardiology
Awarded By
National Institutes of Health
Role
Mentor
Start Date
End Date

Predicting prebiotic effects on human microbiota, behavior, and cognition.

Administered By
Molecular Genetics and Microbiology
Awarded By
Office of Naval Research
Role
Collaborator
Start Date
End Date

Publications:

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation. Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus. Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated. Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay. Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.
Authors
Grzesiak, E; Bent, B; McClain, MT; Woods, CW; Tsalik, EL; Nicholson, BP; Veldman, T; Burke, TW; Gardener, Z; Bergstrom, E; Turner, RB; Chiu, C; Doraiswamy, PM; Hero, A; Henao, R; Ginsburg, GS; Dunn, J
MLA Citation
Grzesiak, Emilia, et al. “Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.Jama Netw Open, vol. 4, no. 9, Sept. 2021, p. e2128534. Pubmed, doi:10.1001/jamanetworkopen.2021.28534.
URI
https://scholars.duke.edu/individual/pub1498711
PMID
34586364
Source
pubmed
Published In
Jama Network Open
Volume
4
Published Date
Start Page
e2128534
DOI
10.1001/jamanetworkopen.2021.28534

Heparin-based blood purification attenuates organ injury in baboons with Streptococcus pneumoniae pneumonia.

Bacterial pneumonia is a major cause of morbidity and mortality worldwide despite the use of antibiotics, and novel therapies are urgently needed. Building on previous work, we aimed to 1) develop a baboon model of severe pneumococcal pneumonia and sepsis with organ dysfunction and 2) test the safety and efficacy of a novel extracorporeal blood filter to remove proinflammatory molecules and improve organ function. After a dose-finding pilot study, 12 animals were inoculated with Streptococcus pneumoniae [5 × 109 colony-forming units (CFU)], given ceftriaxone at 24 h after inoculation, and randomized to extracorporeal blood purification using a filter coated with surface-immobilized heparin sulfate (n = 6) or sham treatment (n = 6) for 4 h at 30 h after inoculation. For safety analysis, four uninfected animals also underwent purification. At 48 h, necropsy was performed. Inoculated animals developed severe pneumonia and septic shock. Compared with sham-treated animals, septic animals treated with purification displayed significantly less kidney injury, metabolic acidosis, hypoglycemia, and shock (P < 0.05). Purification blocked the rise in peripheral blood S. pneumoniae DNA, attenuated bronchoalveolar lavage (BAL) CCL4, CCL2, and IL-18 levels, and reduced renal oxidative injury and classical NLRP3 inflammasome activation. Purification was safe in both uninfected and infected animals and produced no adverse effects. We demonstrate that heparin-based blood purification significantly attenuates levels of circulating S. pneumoniae DNA and BAL cytokines and is renal protective in baboons with severe pneumococcal pneumonia and septic shock. Purification was associated with less severe acute kidney injury, metabolic derangements, and shock. These results support future clinical studies in critically ill septic patients.
Authors
Chen, L; Kraft, BD; Roggli, VL; Healy, ZR; Woods, CW; Tsalik, EL; Ginsburg, GS; Murdoch, DM; Suliman, HB; Piantadosi, CA; Welty-Wolf, KE
MLA Citation
Chen, Lingye, et al. “Heparin-based blood purification attenuates organ injury in baboons with Streptococcus pneumoniae pneumonia.Am J Physiol Lung Cell Mol Physiol, vol. 321, no. 2, Aug. 2021, pp. L321–35. Pubmed, doi:10.1152/ajplung.00337.2020.
URI
https://scholars.duke.edu/individual/pub1484525
PMID
34105359
Source
pubmed
Published In
Am J Physiol Lung Cell Mol Physiol
Volume
321
Published Date
Start Page
L321
End Page
L335
DOI
10.1152/ajplung.00337.2020

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.

Authors
She, X; Zhai, Y; Henao, R; Woods, CW; Chiu, C; Ginsburg, GS; Song, PXK; III, AOH
MLA Citation
She, Xichen, et al. “Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.Ieee Trans. Biomed. Eng., vol. 68, 2021, pp. 2377–88. Dblp, doi:10.1109/TBME.2020.3038652.
URI
https://scholars.duke.edu/individual/pub1489815
Source
dblp
Published In
Ieee Trans. Biomed. Eng.
Volume
68
Published Date
Start Page
2377
End Page
2388
DOI
10.1109/TBME.2020.3038652

Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project's strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study's implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.
Authors
Sperber, NR; Dong, OM; Roberts, MC; Dexter, P; Elsey, AR; Ginsburg, GS; Horowitz, CR; Johnson, JA; Levy, KD; Ong, H; Peterson, JF; Pollin, TI; Rakhra-Burris, T; Ramos, MA; Skaar, T; Orlando, LA
MLA Citation
Sperber, Nina R., et al. “Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network.J Pers Med, vol. 11, no. 7, July 2021. Pubmed, doi:10.3390/jpm11070647.
URI
https://scholars.duke.edu/individual/pub1488830
PMID
34357114
Source
pubmed
Published In
Journal of Personalized Medicine
Volume
11
Published Date
DOI
10.3390/jpm11070647

The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches.

BACKGROUND: Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined. METHODS: In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers. RESULTS: Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection). CONCLUSIONS: Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.
Authors
Steinbrink, JM; Myers, RA; Hua, K; Johnson, MD; Seidelman, JL; Tsalik, EL; Henao, R; Ginsburg, GS; Woods, CW; Alexander, BD; McClain, MT
MLA Citation
Steinbrink, Julie M., et al. “The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches.Genome Med, vol. 13, no. 1, July 2021, p. 108. Pubmed, doi:10.1186/s13073-021-00924-9.
URI
https://scholars.duke.edu/individual/pub1487302
PMID
34225776
Source
pubmed
Published In
Genome Medicine
Volume
13
Published Date
Start Page
108
DOI
10.1186/s13073-021-00924-9

Research Areas:

Antigens
Biological Assay
Biosensing Techniques
Cytoskeletal Proteins
Immune System
Membrane Proteins
Nucleic Acid Hybridization
Pneumonia, Viral